Ahmed Tazim, Karmaker Chitra Lekha, Nasir Sumaiya Benta, Moktadir Md Abdul, Paul Sanjoy Kumar
Department of Industrial and Production Engineering, Jashore University of Science and Technology, Jashore, Bangladesh.
Department of Industrial and Production Engineering, Bangladesh University of Engineering and Technology, Dhaka 1000, Bangladesh.
Comput Ind Eng. 2023 Mar;177:109055. doi: 10.1016/j.cie.2023.109055. Epub 2023 Jan 31.
The recent COVID-19 pandemic has significantly affected emerging economies' global supply chains (SCs) by disrupting their manufacturing activities. To ensure business survivability during the current and post-COVID-19 era, it is crucial to adopt artificial intelligence (AI) technologies to renovate traditional manufacturing activities. The fifth industrial revolution, Industry 5.0 (I5.0), and artificial intelligence (AI) offer the overwhelming potential to build an inclusive digital future by ensuring supply chain (SC) resiliency and sustainability. Accordingly, this research aims to identify, assess, and prioritize the AI-based imperatives of I5.0 to improve SC resiliency. An integrated and intelligent approach consisting of Pareto analysis, the Bayesian approach, and the Best-Worst Method (BWM) was developed to fulfill the objectives. Based on the literature review and expert opinions, nine AI-based imperatives were identified and analyzed using Bayesian-BWM to evaluate their potential applicability. The findings reveal that real-time tracking of SC activities using the Internet of Things (IoT) is the most crucial AI-based imperative to improving a manufacturing SC's survivability. The research insights can assist industry leaders, practitioners, and relevant stakeholders in dealing with the impacts of large-scale SC disruptions in the post-COVID-19 era.
近期的新冠疫情通过扰乱新兴经济体的制造活动，对其全球供应链产生了重大影响。为确保在当前及新冠疫情后时代的企业生存能力，采用人工智能（AI）技术改造传统制造活动至关重要。第五次工业革命，即工业5.0（I5.0）和人工智能（AI），通过确保供应链（SC）的弹性和可持续性，为构建包容性数字未来提供了巨大潜力。因此，本研究旨在识别、评估基于人工智能的工业5.0的当务之急，并对其进行优先级排序，以提高供应链弹性。为实现这些目标，开发了一种由帕累托分析、贝叶斯方法和最佳-最差方法（BWM）组成的综合智能方法。基于文献综述和专家意见，确定了九条基于人工智能的当务之急，并使用贝叶斯-最佳-最差方法（Bayesian-BWM）对其进行分析，以评估其潜在适用性。研究结果表明，利用物联网（IoT）对供应链活动进行实时跟踪是提高制造供应链生存能力的最关键的基于人工智能的当务之急。研究见解可帮助行业领导者、从业者和相关利益相关者应对新冠疫情后时代大规模供应链中断的影响。
